scholarly journals Automatic Transition System Model Identification for Network Applications from Packet Traces

2017 ◽  
pp. 212-227 ◽  
Author(s):  
Zeynab Sabahi-Kaviani ◽  
Fatemeh Ghassemi ◽  
Fateme Bajelan
Keyword(s):  
Model Identification ◽  
Transition System ◽  
System Model ◽  
Network Applications

Fuzzy Labeled Transition Refinement Tree

2014 ◽  
Vol 6 (3) ◽  
pp. 1-31 ◽  
Author(s):  
Sofia Kouah ◽  
Djamel-Eddine Saidouni
Keyword(s):  
Dynamic Systems ◽  
Transition System ◽  
System Model ◽  
Design Parameters ◽  
Concurrent Design ◽  
Incremental Development ◽  
Action Refinement ◽  
Formal Framework ◽  
Labeled Transition System ◽  
Multi Agents

This paper aims to provide a formal framework that supports an incremental development of dynamic systems such as multi agents systems (MAS). We propose a fuzzy labeled transition system model (FLTS for short). FLTS allows a concise action refinement representation and deals with incomplete information through its fuzziness representation. Afterward, based on FLTS model, we propose a refinement model called fuzzy labeled transition refinement tree (FLTRT for short). The FLTRT structure serves as a tree of potential concurrent design trajectories of the system. Also, we introduce bisimulation relations for both models in order to identify equivalent design trajectories, which could be assessed with respect to relevant design parameters.

Identification of Complex System Based on Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 4342-4347
Author(s):  
Zhen Hai Dou ◽  
Ya Jing Wang
Keyword(s):  
Neural Network ◽  
Complex System ◽  
Mean Squared Error ◽  
Model Identification ◽  
Complex Model ◽  
System Model ◽  
Mathematics Model ◽  
Identification Method ◽  
Squared Error ◽  
Actual Output

In order to conquer the difficulty of building up the mathematics model of some complex system, model identification method based on neural network is put forward. By this method, according to actual sample datum, the complex model of crude oil heating furnace is identified at appropriate quantity of net layers and notes. The identification results show that output of model can basically consistent with the actual output and their mean squared error (MSE) almost is 0. Therefore, model identification method based on neural network is an effective method in complex system identification.

Wind power conversion system model identification using adaptive neuro-fuzzy inference systems: A case study

Energy ◽  
2021 ◽  
pp. 122089
Author(s):  
Boudy Bilal ◽  
Kondo Hloindo Adjallah ◽  
Alexandre Sava ◽  
Kaan Yetilmezsoy ◽  
Emel Kıyan
Keyword(s):  
Wind Power ◽  
Fuzzy Inference ◽  
Power Conversion ◽  
Model Identification ◽  
System Model ◽  
Fuzzy Inference Systems ◽  
Neuro Fuzzy ◽  
Conversion System ◽  
Inference Systems

scholarly journals A LTS Approach to Control in Event-B

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Han Peng ◽  
Chenglie Du ◽  
Lei Rao ◽  
Fu Chen
Keyword(s):  
Flow Model ◽  
State Machine ◽  
Transition System ◽  
Control Flow ◽  
Behavior Modeling ◽  
System Model ◽  
Behavior Model ◽  
System Behavior ◽  
Labeled Transition System ◽  
Event Trace

In Event-B, people need to use control variables to constrain the order of events, which is a time-consuming and error-prone process. This paper presents a method of combining labeled transition system and iUML-B to complete the behavior modeling of system, which is more convenient and practical for engineers who are accustomed to using the automaton to build a system behavior model. First, we use labeled transition system to establish the behavior model of the system. Then we simulate and verify the event traces of the labeled transition system behavior model. Finally, we convert labeled transition system model into iUML-B state machine and use it to generate the corresponding control flow model. We use Abrial’s bounded retransmission protocol to demonstrate the practicality of our approach. The simulation results show that the system behavior model generated by the iUML-B state machine has the same event trace as the corresponding labeled transition system model.

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